Distant　supervised　relation　extraction　is　an　efficient　method　to　find　novel　relational　facts　from　very　large　corpora　without　expensive　manual　annotation.　However,　distant　supervision　will　inevitably　lead　to　wrong　label　problem,　and　these　noisy　labels　will　substantially　hurt　the　performance　of　relation　extraction.　Existing　methods　usually　use　multi-instance　learning　and　selective　attention　to　reduce　the　influence　of　noise.　However,　they　usually　cannot　fully　utilize　the　supervision　information　and　eliminate　the　effect　of　noise.　In　this　paper,　we　propose　a　method　called　Neural　Instance　Selector　(NIS)　to　solve　these　problems.　Our　approach　contains　three　modules,　a　sentence　encoder　to　encode　input　texts　into　hidden　vector　representations,　an　NIS　module　to　filter　the　less　informative　sentences　via　multilayer　perceptrons　and　logistic　classification,　and　a　selective　attention　module　to　select　the　important　sentences.　Experimental　results　show　that　our　method　can　effectively　filter　noisy　data　and　achieve　better　performance　than　several　baseline　methods.